The present invention relates to a pattern recognition system for a hand-written character, in particular, relates to such a system which operates in a real-time on-line condition, and recognizes correctly both a simple character and a complicated character having both straight strokes and curved strokes.
There have been known three systems as an on-line pattern recognition system.
The first prior system derives a pair of linear waveforms which is obtained by converting the movement of a hand-written point to rectangular coordinates, and takes an approximation of said linear waveforms through a rectangular function expansion, and recognizes a character by utilizing a coefficient of the rectangular function, (for instance, "On-Line recognition of Handwritten Characters" by Hiroki Arakawa et al, Review of the Electrical Communication Laboratories Vol. 26, Nos. 11-12 November-December, 1978).
The second prior system approximates the strokes of the character to be recognized to a string of vectors, which have eight quantized directions, and classifies the approximated strokes to some fundamental strokes, and recognizes the character by the combination of the fundamental strokes, (for instance, IEEE Transactions on Electronic Computers, December 1967, pages 856-860).
The third prior system classifies the strokes which compose the character to some fundamental strokes, and provides a feature table which describes the character by the end points and/or the cross points of the strokes, and recognizes the input character by comparing the input character with the feature table.
However, the above prior arts have the disadvantages described below.
The first system has the disadvantages that the approximation by the rectangular function is not sufficiently correct for those characters like Chinese characters and/or Japanese alphabet characters which have many straight line components, and the feature in view of the phase of the character is not recognized enough. Therefore, the recognition ratio of this system has not been satisfactory.
The second system has the disadvantages that the classification of the input strokes to the fundamental strokes are not correct enough, and so the recognition ratio is not satisfactory. Further, it takes additional labor to describe the characters to be recognized in detail.
The third system has the disadvantages that the error of the classification of the fundamental strokes provides the decrease of the recognition ratio, as in the case of the second prior system, and it takes additional labor to prepare the feature table for all the characters, and further, a large memory is required to store that feature memory.